A Bayesian approach for image segmentation with shape priors

Color and texture have been widely used in image segmentation; however, their performance is often hindered by scene ambiguities, overlapping objects, or missing parts. In this paper, we propose an interactive image segmentation approach with shape prior models within a Bayesian framework. Interactive features, through mouse strokes, reduce ambiguities, and the incorporation of shape priors enhances quality of the segmentation where color and/or texture are not solely adequate. The novelties of our approach are in (i) formulating the segmentation problem in a well-defined Bayesian framework with multiple shape priors, (ii) efficiently estimating parameters of the Bayesian model, and (iii) multi-object segmentation through userspecified priors. We demonstrate the effectiveness of our method on a set of natural and synthetic images.

• Type: Conference
• Year: 2008
• Where: CVPR
• Authors: Hang Chang, Qing Yang, Bahram Parvin
• Keywords: Computer Vision, CVPR 2008, Image Segmentation Approach, Multi-object Segmentation, Multiple Shape Priors, Segmentation Problem, Shape Priors Enhances